AI-driven investment strategies self-defeat at scale due to signal crowding and erosion.
problem Excess returns from AI-driven investment strategies diminish at scale due to signal crowding and erosion.
method Theoretical model and empirical validation using SEC Form 13F filings and hedge fund return dynamics.
result The alpha half-life of signals decreases significantly with AI adoption, leading to diminishing returns.
This paper extends financial theory to measure learnable market structure under computational constraints.
problem Understanding learnable market structure under bounded computational capacity.
method Introduces financial epiplexity as a measure of learnable market structure, extending classical information theory.
result Proves that equal entropy does not imply equal epiplexity and derives thresholds for useful regimes.
RED CoMETS improves multivariate time series classification accuracy.
problem Complexity of multivariate time series classification.
method Ensemble classifier RED CoMETS for symbolically represented multivariate time series.
result RED CoMETS achieves highest reported accuracy on 'HandMovementDirection' dataset.
High-frequency trading models fail due to overfitting and survivor bias.
problem Failure of hybrid DRL-EC trading systems in high-frequency environments.
method Deployed a population of 500 agents in a high-frequency cryptocurrency environment, analyzing failure modes through multi-disciplinary lens.
result Increasing model complexity without information asymmetry exacerbates systemic fragility.
Study of red blood cells using elastic surface theory.
problem Understanding the shape of red blood cells.
method Used Helfrich-Canham functional to model red blood cells as elastic surfaces.
result Cassinian ovals, except for the round sphere, do not solve the shape equation.
This paper presents the recurrent estimation of distributions (RED) for modeling real-valued data in a semiparametric fashion. RED models make two novel uses of recurrent neural networks (RNNs) for density estimation of general real-valued data. First, RNNs are used to transform input covariates into a latent space to …
ReD improves LLM inference efficiency at fixed budget, reducing attempts and cost.
problem Improving LLM inference efficiency at a fixed budget.
method Reset-and-Discard (ReD) query method.
result ReD increases coverage@cost for a given budget, reducing attempts and cost.
Simple online monitor detects unsafe LLM outputs.
problem LLMs generate unsafe outputs despite training.
method Thresholding external verifier signal to decide alarms.
result Simple design competitive with advanced methods.
Generative ML learns optimal pursuit trajectories in pursuit-evasion games.
problem Optimizing Blue's pursuit trajectory to intercept Red in a game of pursuit-evasion.
method Applying generative machine learning to learn optimal action policies for Blue.
result Generative ML models can learn relevant representations for pursuit-evasion dynamics.
Market Microstructure is the investigation of the process and protocols that govern the exchange of assets with the objective of reducing frictions that can impede the transfer. In financial markets, where there is an abundance of recorded information, this translates to the study of the dynamic relationships between o…
Mathematical model describes how red blood cells return to equilibrium.
problem How red blood cells regain equilibrium after deformation.
method Gradient flow of the Canham-Helfrich functional, proving global existence and convergence for spheres and axisymmetric tori.
result Global existence and convergence of smooth solutions for spheres and axisymmetric tori under specific energy conditions.
SPT predicts age and mass of red giants from spectra.
problem Challenges in age and mass estimation of red giants using traditional methods.
method SPT framework with Multi-head Hadamard Self-Attention and Mahalanobis distance-based loss function.
result Remarkable age and mass estimations with low errors and uncertainties.
Quantization and reduction for coisotropic A-branes on Hamiltonian manifolds.
problem Quantization and reduction of coisotropic A-branes.
method Definition and construction of coisotropic A-branes, and a fibration over reduced spaces.
result Quantization commutes with reduction for coisotropic A-branes.
This study uses deep learning to infer stellar parameters from short TESS and K2 observations.
problem Inferring precise stellar parameters from short-duration TESS and K2 observations.
method Developed a machine learning algorithm to infer asteroseismic parameters from one-month-long TESS observations of red giants.
result The algorithm can accurately infer Δν and νmax for approximately 50% of TESS samples and ΔΠ1 for about 200 young red-giants from K2. Proposes RPG-RT for red-teaming T2I models without internal access.
problem Evaluating T2I models' security through red-teaming is challenging due to their closed-source nature and unknown defense mechanisms.
method Integrates LLM and rule-based preference modeling to dynamically adapt to unknown defense mechanisms.
result Demonstrates superior and practical approach for red-teaming T2I models.
New method classifies reticulocytes from red blood cells without labels.
problem Classifying reticulocytes from mature red blood cells without fluorescent labels.
method Unsupervised machine learning on morpho-rheological markers.
result Promising results in classifying reticulocytes from mature red blood cells.
New framework assesses LLM security risks in BFSI.
problem Lack of domain-specific security evaluation for LLMs in BFSI.
method Risk-aware evaluation framework combining taxonomy, automated red-teaming, and ensemble judging.
result Higher decoding stochasticity and adaptive interaction lead to more severe disclosures.
In this paper, we introduce an insurance ruin model with adaptive premium rate, thereafter refered to as restructuring/refraction, in which classical ruin and bankruptcy are distinguished. In this model, the premium rate is increased as soon as the wealth process falls into the red zone and is brought back to its regul…
Neural framework learns one solution from multiple for combinatorial problems.
problem Finding any one of many possible solutions for combinatorial problems.
method Adapts existing prediction networks to handle solution multiplicity using a selection module trained via RL.
result Framework significantly improves accuracy in solving combinatorial problems.
RED-2400 is a public benchmark of trading events from a Solana exchange, labeled by algorithmic rejection.
problem Analyzing algorithmically-rejected trading events for insights into market dynamics.
method Public dataset of 6,660 algorithmically-rejected trading events, linked to post-rejection price and liquidity trajectories.
result First window of a planned series of datasets extending the time horizon and enabling regime-stratified analysis.
Scenario discovery is the process of finding areas of interest, known as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions, i.e., inputs of the simulation model, where the system is unstable. Subgroup discovery methods are commonly used for scenario discovery. They find…
After shortly recalling the construction of the Khovanov-Kuperberg algebras, we give a characterisation of indecomposable web-modules. It says that a web-module is indecomposable if and only if one can deduce it directly from the Kuperberg bracket (via a Schur lemma argument). The proofs relies on the construction of i…
We introduce \textcolor{red}{general} new techniques for computing the geometric index of a link L in the interior of a solid torus T. These techniques simplify and unify previous ad hoc methods used to compute the geometric index in specific examples \textcolor{red}{ and allow the simple computation of geometric i…
RED detects sleep EEG events using deep neural networks, outperforming previous methods.
problem Manual detection of sleep EEG events is time-consuming and variable.
method Deep Recurrent Neural Networks (RNNs) with convolutional and recurrent components.
result RED outperforms state-of-the-art methods in sleep spindle and K-complex detection.
Word embeddings generated by neural network methods such as word2vec (W2V) are well known to exhibit seemingly linear behaviour, e.g. the embeddings of analogy "woman is to queen as man is to king" approximately describe a parallelogram. This property is particularly intriguing since the embeddings are not trained to a…
We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(m*s+m*log(m)) time complexity, where m is the number of training examples, and s the average number of non-zer…
Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared …
Gradient descent with error feedback performs better than vanilla when features are rare.
problem Improving communication complexity in distributed optimization with rare features.
method Gradient descent with greedy sparsification and error feedback for rare features.
result Communication complexity improves as features become more rare, potentially better than vanilla GD.
In this paper, we obtain analytical expression for the distribution of the occupation time in the red (below level 0) up to an (independent) exponential horizon for spectrally negative Lévy risk processes and refracted spectrally negative Lévy risk processes. This result improves the existing literature in which only…
Subspace clustering aims to cluster unlabeled data that lies in a union of low-dimensional linear subspaces. Deep subspace clustering approaches based on auto-encoders have become very popular to solve subspace clustering problems. However, the training of current deep methods converges slowly, which is much less effic…
We extend the empirical results published in article "Empirical Evidence on Arbitrage by Changing the Stock Exchange" by means of machine learning and advanced econometric methodologies based on Smooth Transition Regression models and Artificial Neural Networks.
New method predicts quasar continuum near Lyman-α with high precision and accuracy.
problem Precise measurement of quasar red damping wing for epoch of reionization.
method Fully probabilistic approach using conditional neural spline flows.
result Achieved state-of-the-art precision and accuracy in predicting quasar continua.
CQL (ReDS) learns from varied driving behaviors, improving offline RL performance.
problem Learning from datasets with non-uniform variability in behavior policies.
method Reweighting the data distribution to allow per-state flexibility in following the behavior policy.
result CQL (ReDS) improves performance in various offline RL tasks.
The paper uses a simulator and optimisation to defend against cyber threats.
problem Defending against cyber threats in simulated networks.
method Dynamic causal Bayesian optimisation (DCBO) integrated with a cyber security simulator.
result DCBO optimally reduces the cost of intrusions in simulated networks.
This paper studies parabolic quasi-Coxeter elements in complex reflection groups and their combinatorial properties.
problem Characterizing and studying parabolic quasi-Coxeter elements in complex reflection groups.
method Defining and characterizing parabolic quasi-Coxeter elements, studying collections of reduced reflection factorizations and relative generating sets.
result Computing cardinalities of collections of reduced reflection factorizations and relative generating sets for large families of parabolic quasi-Coxeter elements.
We present a large catalog of optically selected galaxy clusters from the application of a new Gaussian Mixture Brightest Cluster Galaxy (GMBCG) algorithm to SDSS Data Release 7 data. The algorithm detects clusters by identifying the red sequence plus Brightest Cluster Galaxy (BCG) feature, which is unique for galaxy c…
A new watermarking method corrects bias in language models using maximal coupling.
problem Correcting bias in language model token distributions.
method Maximal coupling to balance bias correction and text quality.
result Outperforms prior techniques in preserving text quality and detectability.
Study of M-theory dual of thermal QCD-like theories at intermediate coupling.
problem Missing top-down holographic dual for thermal QCD-like theories at intermediate 't Hooft coupling.
method Analysis of O(R4) corrections and O(lp6) corrections in the MQGP background. result Discovery of O(R4) corrections and G-structure classification of underlying geometries. Humans reason with concepts and metaconcepts: we recognize red and green from visual input; we also understand that they describe the same property of objects (i.e., the color). In this paper, we propose the visual concept-metaconcept learner (VCML) for joint learning of concepts and metaconcepts from images and associ…
Let (M,ω) be a symplectic manifold, and (Σ,σ) a closed connected symplectic 2-manifold. We construct a weakly symplectic form {ω^{D}}_{(Σ, σ)} on the space of immersions Σ\to M that is a special case of Donaldson's form. We show that the restriction of {ω^{D}}_{(Σ,σ)} to any orbit of the group of Hamiltonian symplectom…
FactorMiner discovers financial alpha factors with low redundancy.
problem Finding novel financial alpha factors in a vast search space.
method Modular Skill Architecture and Experience Memory to distill and guide exploration.
result FactorMiner constructs a diverse library of high-quality factors with competitive performance.
Anti-ELAB protests affected Hong Kong firms' stock prices, especially those linked to pan-democrats.
problem Impact of anti-ELAB protests on Hong Kong firms' stock prices.
method Daily protesting intensity measured by number of protestors from 2019/6/6 to 2020/1/17; analyzed stock price changes of firms.
result Anti-ELAB protests negatively affected firms linked to pan-democrats, positively affected red chips.
New methods correct spectral distortions using known analyte concentrations.
problem Distorted spectral shapes from absorbing and scattering contributions.
method Modified penalized baseline correction methods that incorporate known analyte concentrations.
result Improved prediction performance on near infra-red data sets.
We propose to describe the variety of galaxies from SDSS by using only one affine parameter. To this aim, we build the Principal Curve (P-curve) passing through the spine of the data point cloud, considering the eigenspace derived from Principal Component Analysis of morphological, physical and photometric galaxy prope…
Firms disclosing positive earnings surprises are more likely to disclose ESG information.
problem Transparency vs. performance in financial markets.
method Empirical analysis of earnings surprises and ESG disclosures.
result Positive earnings firms disclose more ESG information than negative earnings firms.
This paper solves a variation of the isoperimetric problem in higher dimensions.
problem Minimizing a weighted perimeter functional with given half-space volumes.
method Introduced a weighted perimeter functional with three weights.
result Characterized two types of minimizers made of spherical domes.
Proposes a method for training Bayesian neural networks using synthetic data from Raman and CARS spectra.
problem Limited real observations in Raman and CARS spectroscopy.
method Log-Gaussian Gamma Processes and Bayesian Neural Networks.
result Trained Bayesian neural networks provide accurate estimates of Raman and CARS spectra with uncertainty quantification.
UK universities pension scheme valuation study shows high dependence on gilt yields.
problem High dependence of UK universities pension scheme on UK government bond yields.
method Analysis of USS valuations from 2014 to 2023, examination of self-sufficiency conditions, and evaluation of metrics.
result Second self-sufficiency condition amplifies gilt yield dependence, leading to inflated liabilities and excessive prudence.